A key advantage of SINDy over deep learning techniques is the reduced data requirements to learn high quality sparse models. To test how MIOSOS extends this advantage, we study the effect of varying the length of the training trajectory on model recovery for three canonical dynamical systems studied in the SINDy literature: the Lorenz model [1]}, the Hopf system [2]}, and a triadic magnetohydrodynamical (MHD) model [3]}.
